Remote Sensing Colorization Based on Bidirectional Macro-Micro Adaptive Enhancement Network

The demand for re-colorization of remote sensing images is urgent since image quality is extremely deteriorated by haze or other noises occurring in the atmospheric layer. The most challenging issue is to restore the color information with respect to preserving spatial consistency as well as to obtain object salience in context with extremely imbalanced space structure, where the former requires learning stable macroscopic semantics while the latter needs to recover microscopic pixels. In this paper, we propose a Bidirectional Macro-Micro Adaptive Enhancement (BMMAEnet) framework by adopting three modules, i.e., the Downward Micro Enhancement (DME) module, the Upward Adaptive Macro Enhancement(UAME) module, and Macro-Micro Balance (MMB) module. Firstly, the DME module is designed by adding micro details as well as suppressing macro context during the multi-branch downsampling process to supplement missing pixel details. Secondly, UAME is proposed by adaptive selecting proper level of features during multi-branch upsampling process to strengthen macro semantic constraints. In addition, MMB is designed by embedding attention-guided local details and global semantics into the decoding features to balance micro and macro information within each branch. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs overpass SOTA methods not only in pixel color value restoration performance but also in human perceptive understanding.